{"id":"W4376872703","doi":"10.2196/47564","title":"User Intentions to Use ChatGPT for Self-Diagnosis and Health-Related Purposes: Cross-sectional Survey Study","year":2023,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":308,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Health care; Generative grammar; Transformer; Psychology; Computer science; Engineering; Artificial intelligence; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003998371,0.0001989442,0.0003696124,0.001091128,0.000603529,0.001003966,0.000333702,0.0007586881,0.00203748],"category_scores_gemma":[0.008545955,0.0004690232,0.0005912711,0.0008884087,0.0005116138,0.001160393,0.0008100657,0.001114819,0.0006865574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004480825,"about_ca_system_score_gemma":0.0005391177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004285084,"about_ca_topic_score_gemma":0.004030668,"domain_scores_codex":[0.9984694,0.0005730178,0.0002493155,0.0001627966,0.0003383797,0.0002070021],"domain_scores_gemma":[0.9911116,0.003560122,0.002460549,0.0004168604,0.001316411,0.001134468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003575504,0.0003712863,0.9931228,0.00004134009,0.00002652165,0.00004754668,0.003748944,0.000033457,0.0001263455,0.00003468251,0.000142696,0.002268715],"study_design_scores_gemma":[0.000008511037,0.0005953852,0.9883637,0.00003251742,0.00002586457,0.0001834292,0.009534559,0.0006534482,0.0001453326,0.00003412232,0.0004095052,0.00001353988],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993032,0.00003522755,0.0001118142,0.00004552836,0.00000242624,0.00005069034,0.0001727112,0.000002777995,0.0002756066],"genre_scores_gemma":[0.9991527,0.00006756723,0.0002511,0.00007206514,0.000003757498,0.00008183429,0.0001700491,0.000001606531,0.0001992365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004285084,"threshold_uncertainty_score":0.02114564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4508828944368118,"score_gpt":0.5279781904950642,"score_spread":0.07709529605825238,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}